AI & Software

Prompt engineering is a real skill with real limits. Understanding why it works helps you know exactly when it will stop.

Switching from Python to Rust won't fix a slow app if the bottleneck isn't your language. Here's what actually determines speed.

Retrieval-Augmented Generation is a genuine improvement over vanilla LLMs. But the AI community has oversold what it actually solves, and teams are paying for that confusion.

If your bug disappears the moment you try to observe it, that's not luck. It's a timing problem wearing a disguise.

Stateless architecture sounds clean and elegant. In practice, it just moves the complexity somewhere else. Here's where it goes.

Intermittent bugs that vanish under scrutiny aren't flukes. They're your system telling you something true about how it actually runs.

A vector is a list of numbers. That's it. Yet embeddings power semantic search, recommendations, and translation. Here's what's actually happening.

Bigger AI models get the headlines, but smaller ones often do the actual work. Here's why compression makes models faster, cheaper, and sometimes smarter.

Embeddings aren't just 'turning words into numbers.' The real idea is stranger and more powerful than that, and understanding it changes how you think about AI.

Everyone celebrates the deployment. Nobody talks about the slow, structural failure that starts the moment real users arrive.

Giving your AI assistant a massive codebase and detailed instructions often produces worse results than a focused prompt. Here's why, and what to do instead.

Most people treat AI like a search engine or a person. It's neither. Fix the mental model first, and the prompts fix themselves.

Every line of code is a liability. Understanding why deletion improves reliability is one of the most practical mental models you can carry into a software project.

Some bugs don't exist until your user count crosses a threshold. Here's why scale creates failure modes that testing simply cannot anticipate.

Async code reduces wait time and increases cognitive load at the same time. That tradeoff is structural, not accidental.

Vector databases don't store meaning. They store geometry. Understanding the difference changes how you build with them.

The concept of 'done' in software is a convenient fiction. Here's why that's not a problem to solve, but a reality to design around.

The skills behind effective prompt engineering aren't new. We've been doing this work for decades under different names.